Association of Novel Sleep EEG Biomarkers with All-Cause Mortality in a Large Community-Based Cohort
Bibliographic record
Abstract
Background: The prognostic value of sleep depth remains poorly understood. The odds ratio product (ORP) is a novel electroencephalogram-based biomarker of sleep depth. We investigated the association between ORP-derived biomarkers and all-cause mortality in a large community-based cohort. Methods: We analyzed 5802 Sleep Heart Health Study participants. A suite of ORP biomarkers was derived from baseline polysomnography, including mean ORP values across sleep stages, change in ORP across the night (ΔORP), interhemispheric sleep depth coherence (ORP Icc R/L ), and ORP architecture phenotypes. Cox proportional hazards models with false discovery rate (FDR) correction estimated mortality associations. Prognostic nomograms were constructed based on variables selected through least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression. Results: During 11.0 years of follow-up, 1305 deaths occurred. After multivariable adjustment and FDR correction, higher ORP W (HR: 0.54, 95% CI: 0.39– 0.73), ORP REM (HR: 0.81, 95% CI: 0.69– 0.95), ORP N1 (HR: 0.71, 95% CI: 0.59– 0.87), ORP ICC R/L (HR: 0.49, 95% CI: 0.29– 0.81), and ΔORP (HR: 0.70, 95% CI: 0.56– 0.87) were associated with lower mortality risk, while higher ORP N3 (HR: 1.38, 95% CI: 1.06– 1.81) predicted increased risk. ORP architecture phenotypes 1,2 (HR: 1.28, 95% CI: 1.06– 1.56), 1,3 (HR: 1.27, 95% CI: 1.05– 1.54), and 3,1 (HR: 1.48, 95% CI: 1.19– 1.84) conferred higher mortality risk compared to phenotype 2,2. Non-linear associations and threshold effects were identified for ORP N1 , ORP ICC R/L , and ΔORP. Among ORP parameters examined, ΔORP and ORP architecture phenotypes were identified as the most important predictors through LASSO and multivariable Cox regression. Prognostic nomograms integrating these selected ORP metrics with traditional risk factors demonstrated excellent discrimination (C-index: 0.81). Conclusion: ORP-derived biomarkers are independently associated with all-cause mortality and complement conventional sleep metrics in refining mortality risk stratification. Identified threshold effects for several ORP parameters may provide potential cutoff points for clinical intervention. Keywords: EEG biomarkers, sleep depth, odds ratio product, all-cause mortality
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".